论文部分内容阅读
在灰色微分动态模型的基础之上,采用季节/年际性指数对原始降水和实测径流进行预处理,并引入自记忆函数,构建灰色微分动态自记忆模型,将其应用于滦河流域径流过程的模拟和预测。结果表明:(1)采用预处理前的降水径流数据所构建的DHGM(2,2)模型和DHGM(2,2)自记忆模型在年尺度和月尺度上的径流模拟效果较差,难以反映径流的变化过程,对输入数据进行预处理后,构建的DHGM(2,2)自记忆模型模拟精度得到了很大的提高,三道河子站和滦县站年径流和月径流模拟序列的Nash-Sutcliffe系数和相关系数均达到了0.6以上;(2)模型在年尺度和月尺度的径流预测中具有一定的适用性,且结构简单、计算方便,但需要进一步考虑蒸发、土地利用和人类活动等因素,使模型更为完善。
Based on the gray differential dynamic model, the seasonal precipitation and measured runoff are pre-treated by using seasonal / interannual index, and the self-memory function is introduced to construct the gray differential dynamic self-memory model, which is applied to runoff process in Luanhe River Basin Simulation and prediction. The results show that: (1) DHGM (2,2) model and DHGM (2,2) self-memory model constructed by pre-pre-processing precipitation runoff data have poor runoff and monthly runoff simulation results and are difficult to reflect After the preprocessing of the input data, the simulation accuracy of DHGM (2,2) self-memory model constructed has been greatly improved. The annual runoff and monthly runoff simulation series of Sandaohe Station and Luanxian Station The Nash-Sutcliffe coefficient and the correlation coefficient all reach more than 0.6. (2) The model has some applicability in the runoff prediction of annual and monthly scales and has the advantages of simple structure and convenient calculation, but requires further consideration of evaporation, land use and human Activities and other factors, make the model more perfect.